This report is a rough analysis of all the 211 calls made between Feb 25, 2016, and Feb 9, 20171 If I understood the data correctly, the new system is still in testing before May 2016, which was when calls started steadily coming in.. All the numbers and facts are for the whole time span, i.e., they are about the whole landscape of human service needs reached the Mass 211 system in a roughly 10 month period.
| Have you used Mass211 before? | How did you hear of Mass211? | AIRS I&R Need Category | Referrals Made |
Above graph shows where the calls were coming from and where the callers were referred to. The majority of them are from first-time callers referred by human service agencies. Housing and Income Support/Assistance are the two most inquired issues.2 Most Income Support/Assistance requests are about the Early Education and Care program (see below).
About one-third of all calls had more than one Taxonomy term, and more than 47% callers were given multiple referrals.
Top 10 Level 5 Taxonomy term combinations
| Categories | Num. of Calls | % of all calls |
|---|---|---|
| Electric Service Payment Assistance · Government Consumer Protection Agencies |
258 | 0.83 |
| Child Care Expense Assistance · Early Head Start Sites |
189 | 0.61 |
| Child Care Expense Assistance · Family Support Centers/Outreach * Children |
142 | 0.46 |
| Child Care Centers · Child Care Expense Assistance |
124 | 0.40 |
| Homeless Shelter · Housing Search Assistance * Tenants |
124 | 0.40 |
| Electric Service Payment Assistance · Utility Service Complaints |
120 | 0.39 |
| Child Care Expense Assistance · Head Start Sites |
99 | 0.32 |
| Electric Service Payment Assistance · Heating Fuel Payment Assistance |
95 | 0.31 |
| Child Care Expense Assistance Applications · Early Head Start Sites · Family Support Centers/Outreach * Children |
85 | 0.28 |
| Child Care Expense Assistance Applications · Family Support Centers/Outreach * Children |
84 | 0.27 |
The most common case of multiple purpose calls were those asking for child care assistance programs, then information regarding child care providers (child care centers or early start sites) were also provided.
Another common case is people making complaints about utility companies while seeking financial assistance in utility payment.
There are many ‘useless’ calls in this category.
Top 10 Level 5 Taxonomy terms for single purpose call
| Taxonomy Term | Num. of Calls | % of all calls |
|---|---|---|
| Child Care Expense Assistance | 8116 | 26.26 |
| Child Care Expense Assistance Applications | 1233 | 3.99 |
| Homeless Shelter | 1029 | 3.33 |
| 211 Systems | 1002 | 3.24 |
| Rent Payment Assistance | 989 | 3.20 |
| Electric Service Payment Assistance | 821 | 2.66 |
| Directory Assistance | 407 | 1.32 |
| Food Pantries | 275 | 0.89 |
| Food Stamps/SNAP Applications | 259 | 0.84 |
| Housing Search Assistance * Tenants | 225 | 0.73 |
Child care related calls took the largest portion of the caseload, not only because that Mass 211 has a unique collaboration with Massachussets Department of Early Education and Care (EEC),3 I have found no other states are having the same high-volume of calls related to child care. but also because some parents frequently called to check their enrollment application for EEC subsidies.4 About 20% of child care related calls were explicitly marked as status check. It seems we may need to better inform parents with the expected processing time, if there indeed exists an expectation.
“211 Systems” are mostly border-line-crossing calls from neighboring states; “Directory Assistance” are those intended for 411. They both seem unavoidable.
Putting these categories aside, the most frequently inquired human services are EEC Applications, Homeless Shelter/Rent Assistance, Electricity, and Food.
Average call length of all calls is 8.3 minutes. Most calls (75%) finish within 10 minutes, 95% of them finish within 20 minutes. Some took as long as 40 minutes or more, but those were rare cases and often involved interpretation services (the caller didn’t speak English).
When removed undesired calls (211 Systems and Directory Assistance), which normally end fast, the average call length becomes 8.8 minutes.
Mean, median and quantiles of call length
| minimum | q1 | median | mean | q3 | maximum |
|---|---|---|---|---|---|
| 0.5 | 3.5 | 6.5 | 8.334 | 10.5 | 403.5 |
If taking call length into consideration, the list of top categories by caseload would be different. Following are the average call length and number of calls aggregated to the Level 5 Taxonomy term.
The most time-consuming calls
| Taxonomy Term | Avg. call length | Num of calls | Minutes per day |
|---|---|---|---|
| Child Care Expense Assistance | 7.83 | 10436 | 302.82 |
| Rent Payment Assistance | 9.95 | 5601 | 206.45 |
| Homeless Shelter | 10.95 | 4838 | 196.21 |
| Electric Service Payment Assistance | 9.43 | 4592 | 160.41 |
| Child Care Expense Assistance Applications | 13.28 | 2054 | 101.05 |
| Food Pantries | 11.77 | 1913 | 83.37 |
| Heating Fuel Payment Assistance | 11.71 | 1571 | 68.13 |
| Food Stamps/SNAP Applications | 10.92 | 1635 | 66.11 |
| Housing Search Assistance * Tenants | 10.44 | 1491 | 57.65 |
| Family Support Centers/Outreach * Children | 13.64 | 923 | 46.63 |
Note that for calls with multiple purposes, their call lengths are evenly divided for each purpose.
Use above interactive treemap to explore the average total time consumption per day for each category. Click on the tiles to enter a subcategory, then click on the title bar at the top to go back.
Since most of the 211 services are about providing help to needy families and individuals, the geographic distribution of 211 calls strongly correlates with the income level of a region.
A multiple linear regression was calculated at the zip code level to predict the number of calls per 1,000 residents based on their income, race, and education level.
The variables chosen are percentages of people who completed at least a Bachelor’s degree (education_bachelor), percentages of white people (race_white), and median house income measured in 1,000 US dollars (income). An interaction term race_white:income is also added to reveal interactions between the effects of house income and race.
| Dependent variable: | |
| Number of calls per 1,000 people | |
| Education: Bachelor and Above (%) | -0.038*** |
| (0.011) | |
| Race: White (%) | -0.184*** |
| (0.018) | |
| Median House Income (1K USD) | -0.151*** |
| (0.029) | |
| White:MedianHouseIncome | 0.001*** |
| (0.0003) | |
| Constant | 22.628*** |
| (1.388) | |
| Observations | 484 |
| R2 | 0.509 |
| Adjusted R2 | 0.505 |
| Residual Std. Error | 2.921 (df = 479) |
| F Statistic | 124.012*** (df = 4; 479) |
| Note: | *p<0.1; **p<0.05; ***p<0.01 |
A significant regression equation was found (F(4, 472) = 121.11, p < 0.000), with a \(R^2\) of 0.502.
The predicted number of calls per 1,000 people during a 10-month period is equal to 22.628 - 0.038 (education_bachelor) - 0.184 (race_white) - 0.151 (income) + 0.001 (race_white:income).
The number of calls would decrease 0.038 per 1 percent increase in people with a Bachelor’s degree, 0.184 per 1 percent increase in white residents, and 0.151 per $1,000 increase in median house income.
For different races (white and non-white), the effect of income may differ, but the difference is small (only 0.001)–at the same income level, white people is slightly more likely to call 211 than other races.
A call may naturally have multiple purposes, but does a call for certain service predict the coming of other types of needs?
To answer this question, we can check the concordance of need categories, i.e. the correlation between volumes of calls in different categories at a given region.
Take the most requested two service categories for example, zip code areas with higher demands for Income Support (Child Care Assistance) almost always have a high demand for Housing Assistance (Kendall’s tau coefficients \(r_{\tau}\) = 0.471, p < 0.000).
The same strong correlation can be found at most AIRS Need Category pairs.
Concordance between service categories
This graph shows Kendall’s tau coefficients for each pair of AIRS Problem/Need categories. The darker the blue, the more in agreement the two are. Transparent tiles represent insignificant results, mostly because of low call volume. To minimize bias, zip codes with no call in one of the categories were removed before calculation.
The most obvious pairs are Art/Culture/Recreation + Clothing/Personal/Household Needs, Employment + Volunteers/Donations, Disaster Services + Volunteers/Donations, and Housing + Income Support.
It is not immediately clear whether calls of different categories are from the same group of people or not.
This report is based on the iCarol reports generated on Feb 9, 2017. The data were manually cleaned and transformed, with a few noteworthy controls:
The Taxonomy provides a comprehensive and logical structure for human services, but is not suitable for high-level analyses–the end-level terms are too granular (there are 830 of them), and the upper levels too broad and not self-explanatory.
The AIRS: I&R Problem/Needs National Categories6 The linked document is outdated. It contains only 16 categories, but AIRS has split “Housing/Utility” into two separate categories in 2014, making it 17 categories in our case. as seen in the MetUnmet report is a better candidate for reporting, and was used in the flow chart at the beginning fo this document, but they are also not revealing enough. For instance, “Income Support” does not reveal the fact that the majority need is Early Education and Care.
That is why I used AIRS Neet Categories for general analaysis, but Level 5 Taxonomy terms to reveal more details in call purposes. Another solution is to create a flat, topic-based categorization method that aligns with the unique demands Massachusetts constituents. Curating such a list is a time-consuming process and would need vigorous validation.
This report presents the big picture of the state’s human service needs of the past year and created graphs and tools to explore some of the basic characteristics of the data.
The next step is to cross-validate with more data sources and start analyzing other data fields such as age, sex, and family characteristics.
But before that, building a simple and robust typology is yet still an unresolved challenge. I tried some name matching with Regular Expressions, but it did not work well for all cases.
However, if we are satisfied with current big picture presentation, we may also choose to dive into specific topics immediately. That is, to isolate calls related to a certain topic, for instance, people with mental health and substance abuse issues, then conduct comprehensive analysis in caller profiling, longitudinal trend, etc.